Real-Time Information Extraction for Phone Review in Car Loan Audit

Hongxuan Liu, Jie Wang, Yansong Wang, Shuling Yang, Hanzhu Chen, Binbin Fang

Abstract: Phone review is important in car loan audits, in which auditors contact applicants to make risk assessments by how applicants act to a sequence of questions. Due to the length of dialogues, auditors tend to miss important details, thus requiring an aiding system to record the dialogues in a compact form. Existing methods that utilize slot-value pairs to track the latest dialogue states fail to record the intermediate process which is critical for risk assessment. In this paper, we propose quadruples which consist of a dialogue act and a triple in a concept graph to represent the dialogue process, and model the dialogue recording task as a quadruple extraction problem for each utterance. To concisely construct quadruples, we convert slot-value pairs into a concept graph by disentangling domains from slots. In order to extract quadruples in real time, we design a model incorporating multi-head cross-attention mechanism and embedding sharing while considering parameter size and inference speed. Experiments on our real-world dialogue dataset show that our model achieves an accuracy of $$\sim $$ 82.7% which is similar to the best baseline with only $$\sim $$ 30 M parameters while performing real-time inference $$\sim $$ 3.6 times faster on an 8-core CPU with $$\sim $$ 90 ms per utterance.